03-back-to-the-basics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "from fastai.vision import *\n",
    "from fastai.metrics import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Back to the basics: Multilayer Perceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bei der Klassifikation von Bildern können wir mit CNN und einer Menge kleiner Tricks und Best Practices sehr gute Ergebnisse erreichen. Aber wie funktioniert die Klassifikation, wenn wir nicht in die Trick-Kiste greifen? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ein Klassiker unter den Datensätzen: MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wir starten mit einem \"einfachen\" Datensatz, der auch ohne Tricks beherrschbar ist. Der MNIST-Datensatz enthält ein Sammlung von handgeschriebenen Ziffern. Die Daten sind bereits in einen Traininsdatensatz und einen Validierungsdatensatz aufgeteilt."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datenaufbereitung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/jupyter/.fastai/data/mnist_png/testing'),\n",
       " PosixPath('/home/jupyter/.fastai/data/mnist_png/training'),\n",
       " PosixPath('/home/jupyter/.fastai/data/mnist_png/models')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MNIST_path = untar_data(URLs.MNIST)\n",
    "MNIST_path.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Ziffern liegen jeweils in einem eigenen Ordner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/jupyter/.fastai/data/mnist_png/training/3'),\n",
       " PosixPath('/home/jupyter/.fastai/data/mnist_png/training/1'),\n",
       " PosixPath('/home/jupyter/.fastai/data/mnist_png/training/7'),\n",
       " PosixPath('/home/jupyter/.fastai/data/mnist_png/training/8'),\n",
       " PosixPath('/home/jupyter/.fastai/data/mnist_png/training/0'),\n",
       " PosixPath('/home/jupyter/.fastai/data/mnist_png/training/6'),\n",
       " PosixPath('/home/jupyter/.fastai/data/mnist_png/training/9'),\n",
       " PosixPath('/home/jupyter/.fastai/data/mnist_png/training/2'),\n",
       " PosixPath('/home/jupyter/.fastai/data/mnist_png/training/4'),\n",
       " PosixPath('/home/jupyter/.fastai/data/mnist_png/training/5')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(MNIST_path / 'training').ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wir laden mit einer Minibatch-Größe von 64..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x792 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = (ImageList.from_folder(MNIST_path, convert_mode='L') # convert_mode='L' erzeugt einen Channel (und nicht drei für RGB)\n",
    "        .split_by_folder(train='training', valid='testing')\n",
    "        .label_from_folder()\n",
    "        .transform(size=28)\n",
    "        .databunch(bs=64))\n",
    "data.show_batch(rows=8, figsize=(15,11))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wir haben 60.000 Beispiele in unserem Trainingsdatensatz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.train_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Validierung erfolgt mit 10.000 Beispielen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.valid_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Anzahl der Beispiele pro Klasse sind im Trainings- und Testdatensatz einigermaßen ausbalanciert..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({3: 6131,\n",
       "         1: 6742,\n",
       "         7: 6265,\n",
       "         8: 5851,\n",
       "         0: 5923,\n",
       "         6: 5918,\n",
       "         9: 5949,\n",
       "         2: 5958,\n",
       "         4: 5842,\n",
       "         5: 5421})"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collections.Counter(data.train_ds.y.items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({3: 1010,\n",
       "         1: 1135,\n",
       "         7: 1028,\n",
       "         8: 974,\n",
       "         0: 980,\n",
       "         6: 958,\n",
       "         9: 1009,\n",
       "         2: 1032,\n",
       "         4: 982,\n",
       "         5: 892})"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collections.Counter(data.valid_ds.y.items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das 42. Bild aus den jeweiligen Datensätzen können wir so anzeigen..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.train_ds.x[42].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.valid_ds.x[42].show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Design und Training des neuronalen Netzes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wir erzeugen ein einfaches mehrlagiges Perzeptron (Multilayer Perceptron).\n",
    "\n",
    "* input layer: 784 (=28x28)\n",
    "* first hidden layer: 400\n",
    "* second hidden layer: 64\n",
    "* output layer: 10\n",
    "\n",
    "Das Netz wird mit einer eigenen Klasse definiert. Im Konstruktor der Klasse werden die Schichten durch die zu trainierenden Gewichte definiert. Die eigentliche Berechnung wird in der Methode `forward` definiert. Schaue dir die Dokumentation der genutzten Objekte und Funktionen an."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MNIST_MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.hidden1 = nn.Linear(28*28, 400, bias=True) # input -> 1st hidden layer\n",
    "        self.hidden2 = nn.Linear(400, 64, bias=True) # 1st hidden layer -> 2nd hidden layer\n",
    "        self.output = nn.Linear(64, 10, bias=True) # 2nd hidden layer -> output layer\n",
    "\n",
    "    def forward(self, xb):\n",
    "        x = xb.view(-1, 28*28) # input layer\n",
    "        x = torch.sigmoid(self.hidden1(x)) # 1st hidden layer after activation\n",
    "        x = torch.sigmoid(self.hidden2(x)) # 2nd hidden layer after activation\n",
    "        x = torch.softmax(self.output(x), dim=1) # output layer after activation\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jetzt können wir unser neu erstelltes Netz trainieren."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = Learner(data=data, model=MNIST_MLP(), loss_func=nn.CrossEntropyLoss(), metrics=error_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.539417</td>\n",
       "      <td>1.531049</td>\n",
       "      <td>0.063400</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.513461</td>\n",
       "      <td>1.513516</td>\n",
       "      <td>0.049300</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Das Netz scheint nacht dem kurzen Training (2 Epochen) mit den Grundeinstellungen schon sehr gute Ergebnisse zu liefern. Wir könnten das Netz durch einen erneuten Aufruf von `learn.fit_one_cycle(n)` für weitere `n` Epochen trainieren. Falls dabei etwas schief geht (z.B. falls die error_rate plötzlich wieder ansteigt), können wir von Vorne beginnen, in dem wir den Learner einfach neu erstellen. Die Gewichte werden dabei zufällig initialisiert und lassen sich z.B. mit `learn.model.hidden1.weight` anzeigen. \n",
    "\n",
    "Schauen wir uns die Konfusionsmatrix nach dem ersten Training an, die eine schöne Diagonale zeigen sollte."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3ZXx+hmutHeHrMUVERAKd3gUjIiLiABWuiIiIA1S4IiIiDlDhioiIOECFKyIi4gAVroiIiANUuCIiIg5Q4YqIiDhAhSsiIuIAFa6IiIgDVLgiIiIOUOGKiIg4QIUrIiLiABWuiIiIA1S4IiIiDlDhioiIOECFKyIi4oAgtwMEiuPbnnA7Qp1FpP7d7Qjn5NjiW92OcE6MMW5HED9hrXU7QoPW0PY1neGKiIg4QIUrIiLiABWuiIiIA1S4IiIiDlDhioiIOECFKyIi4gAVroiIiANUuCIiIg5Q4YqIiDhAhSsiIuIAFa6IiIgDVLgiIiIOUOGKiIg4QIUrIiLiABWuiIiIA1S4IiIiDlDhioiIOKBBFe6CV+YT3T7S7Rh1MuPO6SQlxDN50kRKSkpczdI8JJgNj47laPpNxHRuBcD4uK6sn53Ka78fTac2zQDoER3B2ofHsH52KomXdQSgWdMg0u8aztqHxzB9fC/XfoazlZeXM+XGySQnDiJlyGA++vBDtyN5tXvXLhIHxZIyZDCpo0dy4sQJtyN55U9zt64C5diwbdtWhiUnMiw5kcsu7sGd06e6HalWG3LWM2JYMkOTElixfJnbcbxye+76tHCNMf9tjHndGJNjjEk3xgT7cnxvysvLWbI4g06dop1a5XnL276dw4cOsXb96/SMiWHxogxX83xdXMrY+19lyaaKYgpq5OF/x1xGym+Xcf+8rcy4ujcA91/Xj5v/L5tRs1Yya2JfAG4cFsOr2z4h6TdLib+kAx1bN3Pt5zhjx448iouLycrewIyZd/Pk359wO5JX3bp3J3vDRjLX5dC7T1+WL13idqQa+dvcrYtAOjb06dOX1VnZrM7KZmBcHKNGj3E7klenTp3iz3P+xNIVr7Jm7XpGjb7K7Ug18oe56+sz3M+AYdbawcAHgGOzZcH8l0kdOx6Px/9P2rfkbiY5ZSgAKUOHk7t5k6t5ysotXxSeqvr6og5hvPvpMU6XlrN5zyEu6VJx1hsVEcqHnxdQ9PVpviw6ReuWTenXox1rtx8AYF3eQfp2b+fKz3C2jh07AWCtpSA/nzZt/PvMJjj4m+elJ0+epFv3Hi6m8c7f5m5dBNKx4YzS0lK2bdlCbFy821G8yt28iZCQEManjubq8WM5dOiQ25Fq5A9z16cz0Fp7yFp7svLL00CpL8evSVlZGYsy0pmQdrUTq/vBCgryadGyJQBhYWEcP37M5UTfFt6sMUUnT1d93chjADDmm2UKvyohonkTwpo1ofBkxaWZgpMlRLRo4mjW6rRp0waPx0OvS2O4a+ZvuPa6yW5HqtXarEz69+5FzvpsLuza1e04NfL3uftdgXZsOGN99jriBg3y+ycJRw4fZv/+j8lYspwbb5rCgw/c63akGvnD3K2Xf01jTGcgGVj5nfsnVF5qTj944IDP1jd/3kuMG5/m95PzjPDwCIoKCwHIz88nIqKVy4m+7fiJYlqEfnPWVVZuASgv/2aZsOaNOX6imPwTxbQMbQxUFPXxomJHs1Ync81qmoY0JW/XHl5ekMGMO6a5HalWSckp5L65ndRx45n7zFNux6mRv8/d7wq0Y8MZixctZOy4CW7HqFVYeDgDY2Np3LgxCYlDeG/PHrcj1cgf5q7PZ6ExpiXwInCDtfb02Y9Zaxdaa9OstWmdon33esqePe8y76UXGD1yOB988D53TPPvNxr07defrMw1AGRlrmbAwFiXE33bh58X0jO6FcFBHgb0jOKd/V8CcDj/JF3bh9EiJJhWzZvyZeEptu49TFKvin/LxMs6seW9w25GrxIREQFAeFg4+QX5Lqfxrrj4mycpYS3DaBbq/uvgNfH3uftdgXZsgIrLyVtzc4mLH+R2lFr17tO3qmR35G3nggsudDlRzfxh7gb5cjBjTCNgHnC/tXafL8f25sGHHq66HduvN7Mfm+PUqs/L5b160S4qiqSEeKKjOzN12h1uR2LJPSO47II2dOsYzjOr3uWJ5TvJfOgqTpWUcdOctQDMemELT92eSCOP4f552wB4ds0e/vnrJK5L6s6r2z7h38e+cvPHACA5ZSjz573E0KQEiouLeXj2Y25H8mptViZzHpuNx+OhTWQkT899zu1INfLHuetNoB0bAHLWZxMbHx8QZ+WtW7dmxJWjSBkyGI/Hw5NPzXU7Uo38Ye4aa63vBjPmGuAJ4J3Ku/5urV1Q3bLjJqTZefPTfbZu+UZE6t/djnBOji2+1e0I58Sc/WK2/Efz5fFTvi+Q9rWJ16SxaGG618A+PcO11s4H5vtyTBERkYbA/69ZiIiINAAqXBEREQeocEVERBygwhUREXGACldERMQBKlwREREHqHBFREQcoMIVERFxgApXRETEASpcERERB6hwRUREHKDCFRERcYAKV0RExAEqXBEREQeocEVERBygwhUREXGATz+AXvzDl4tudTvCOYmc+LzbEc7JFy9PdjvCOSkrt25HOCeNPMbtCHVmA2vTYgJn0zZIOsMVERFxgApXRETEASpcERERB6hwRUREHKDCFRERcYAKV0RExAEqXBEREQeocEVERBygwhUREXGACldERMQBKlwREREHqHBFREQcoMIVERFxgApXRETEASpcERERB6hwRUREHKDCFRERcYAKV0RExAENpnCLioqIH9iPNuHN2b1rl9txajXjzukkJcQzedJESkpK3I5TraKiIgbH9adtqxbs3l2xTTMWLiBx0ECuGJbEwQMHXM3nMYa5v4zntXuG8+TP4whqZOjQKpQFdw7htXuG89vxl31r+aUzU3hwUm+X0tYsEOZuUVERCXH9aXfWXAA48OmntGrR9Fv3+Rvta/Vn27atDEtOZFhyIpdd3IM7p091O1KNdu/aReKgWFKGDCZ19EhOnDjheAafFq4x5hJjzEZjTI4x5l/GmOa+HN+bkJAQFi9bSerY8U6t8rzlbd/O4UOHWLv+dXrGxLB4UYbbkaoVEhJCxpIVjKncpqdPn+bxP89hddZ6Zt1zP3/8wwOu5ruqX2f2HznBFfet4r2D+VzV90c8OKk3tz+dyxX3reKhjB1Vy/brFol1Mas3gTB3Q0JCWHjWXDjjT48+TP+BsS6lqp32tfrVp09fVmdlszorm4FxcYwaPcbtSDXq1r072Rs2krkuh959+rJ86RLHM/j6DHevtTbWWjsY2Aqk+nj8GgUFBREZGenU6n6QLbmbSU4ZCkDK0OHkbt7kcqLqfXebfvDB+/SMiaFx48YMGBjr+llNl3Yt2Ln/GAB5H39JbEw7Okc256Hr+vCvWcPo1+2b7D8fEcNTq/e4FdWrQJi71WXc//HHGGOIju7sUqraaV9zRmlpKdu2bCE2Lt7tKDUKDg6uun3y5Em6de/heAafFq619vRZX4YC7/ly/IaioCCfFi1bAhAWFsbx48dcTlQ3Bfn5tGjRsurrsrIyF9PA3oMFDL4kCoDESzvQrUMYl3SOYOaL27jhzxt4eHJfAAb2aMs7nxzjxNelbsZtcP706MP8aup0t2N4pX3NGeuz1xE3aBAej3+/Srk2K5P+vXuRsz6bC7t2dXz9Pt86xpgUY8x2IBH48DuPTTDGpBtj0v31NQknhIdHUFRYCEB+fj4REa1cTlQ34eERFBUVVn3dqFEjF9PAa28foKS0nFdnDaNZkyB27j/GB58X8u9jJzlS8DWlZZZGHsNtIy/mH6v03M+XPvqwYtf+UZcu7gaphfY1ZyxetJCx4ya4HaNWSckp5L65ndRx45n7zFOOr9/nhWutzbTW9gIygJu/89hCa22atTatU3S0r1cdMPr2609W5hoAsjJXM8CPXwM7W9eLLuK9PXsoKSlh86aNXHLJpa7msRZmPL+NEfev5tiJYlZs/ZSCr0poGRJMaJMgGgd5KCu3XNCuBS9MTeCBa/+bq/r9iKG9OrqauyF4550d7NnzLmOuvIJ1azO5/bafcfr06dq/0WHa1+pfaWkpW3NziYsf5HYUr4qLi6tuh7UMo1loM8czBPlyMGNME2vtmZ+qAGjsy/FrM2bUCHbsyGPfvr3cNOUWJl0/2cnV19nlvXrRLiqKpIR4oqM7M3XaHW5HqlHq6JHs3JnH+/v2cuNNN/OLX/6KYckJNG3alKfnPu9qtrZhITx/+2BKy8rJfudzNu89wn2vvE3GjGSCGnl4YMF2AAbcuRyA+Jgohv93J9Zs/8zN2NUKhLk79jtzIXPdBgBuuekG/nfqtG+9RuYvtK/Vv5z12cTGxwfE5eQ5j83G4/HQJjKSp+c+53gGY63v3rtpjLkSuAMoB44Ck621J6tbdtyENDtvfrrP1i3fKC/31/fjVq/ttf57MKnOFy9PdjvCOSkLsPnQyGPcjlBngbavmcDZtACYAAo88Zo0Fi1M9xrYp2e41tqVwEpfjikiItIQ+Pc1ABERkQZChSsiIuIAFa6IiIgDVLgiIiIOUOGKiIg4QIUrIiLiABWuiIiIA1S4IiIiDlDhioiIOECFKyIi4gAVroiIiANUuCIiIg5Q4YqIiDhAhSsiIuIAFa6IiIgDVLgiIiIO8OkH0It/MMbtBOfmi5cnux3hnLS77kW3I5yTwy9McjtCg+XxBNjOFmDKyq3bEerM1iGqznBFREQcoMIVERFxgApXRETEASpcERERB6hwRUREHKDCFRERcYAKV0RExAEqXBEREQeocEVERBygwhUREXGACldERMQBKlwREREHqHBFREQcoMIVERFxgApXRETEASpcERERB6hwRUREHKDCFRERcUCDKdwZd04nKSGeyZMmUlJS4nacWhUVFRE/sB9twpuze9cut+PUakPOekYMS2ZoUgIrli9zO06t/HE+eIzh6V/EsfLuFP52y0CCGhkAOrUO5cjzP6Fnp3AABnRvy9r7ryDzvuH8Zux/uRm5WoE2dwEWvDKf6PaRbseo1e5du0gcFEvKkMGkjh7JiRMn3I7k1dtvvUVSQjwpQwYz8Zo0Tp8+7Xak7ykqKiIhrj/tWrVg9+5dlJSUMDwlkeEpiSTE9Wdg3//nWJZ6KVxjzDXGmKP1MXZ18rZv5/ChQ6xd/zo9Y2JYvCjDqVWft5CQEBYvW0nq2PFuR6nVqVOn+POcP7F0xausWbueUaOvcjuSV/46H0b3jWb/kSKu/H0mez/LZ3SfzgDcPuoStuz7Znf53ytjuPXJjaTcs4rES9sTFhrsVuRqBdLcBSgvL2fJ4gw6dYp2O0qtunXvTvaGjWSuy6F3n74sX7rE7UhedejYkRWvriZzXQ5du17E8mVL3Y70PSEhISxcsoIxlfO1cePGrMrMZlVmNrf+/DaudPB45vPCNcZ4gPHAAV+PXZMtuZtJThkKQMrQ4eRu3uTUqs9bUFAQkZH+/4wbIHfzJkJCQhifOpqrx4/l0KFDbkfyyl/nQ5e2LXhn/3EAduw/xoAebflRZHMslgNffFW13N7PCmgZ0pigRoaycsup02VuRa5WIM1dgAXzXyZ17Hg8Hv+/oBcc/M2Tq5MnT9Ktew8X09QuKiqK0NBQoCJ7UFCQy4m+z9t8XbIog9RxExzLUh8z8CdABlBeD2NXq6AgnxYtWwIQFhbG8ePHnFr1f4Qjhw+zf//HZCxZzo03TeHBB+51O5JX/jof9n5WwKCLowBIuKQ9YaGNuX3UxTy+8t1vLbfyzQO8ePsg3nz0Kt549xDFpx3blRqcsrIyFmWkMyHtarej1NnarEz69+5FzvpsLuza1e04dfLpp5+ybm0WI0Ze6XaUOisqKuLgwQP07Bnj2Dp9WrjGmEZAGrCghscnGGPSjTHpBw/47gQ4PDyCosJCAPLz84mIaOWzsQXCwsMZGBtL48aNSUgcwnt79rgdySt/nQ+rth+kpLScFXelENokiNAmFWcDn551dgvw4MT/5or719Dr10u5uHME3Tq0dCNugzB/3kuMG58WEGe3ZyQlp5D75nZSx41n7jNPuR2nVoWFhfx08iSeeubZb52h+7tXVy5nxJWjHF2nr2fhtUC6tbbap+TW2oXW2jRrbVqnaN+9ntK3X3+yMtcAkJW5mgEDY302tkDvPn2rSnZH3nYuuOBClxN556/zwVqY+dKbjHowk2Mnitm5/xg9OoWx6DdDSLy0PXN+2o+gRoZyaynOyOxNAAAgAElEQVQ8WYK1cOLUaVqGNnY7esDas+dd5r30AqNHDueDD97njmlT3Y7kVXFxcdXtsJZhNAtt5mKa2pWVlXHDdROZefcsftytm9txzonTl5MBfH3BPQboZYy5FvixMWaOtbbeZ/jlvXrRLiqKpIR4oqM7M3XaHfW9Sp8YM2oEO3bksW/fXm6acguTrp/sdqRqtW7dmhFXjiJlyGA8Hg9PPjXX7Uhe+et8aBvWlH/+Mp6ycsv6dz5nzordPLLkHQD+dstAHv/Xu5SWWR5evJOFdw6htNzywb8LefODL1xO/n2BMncffOjhqtux/Xoz+7E5Lqap3dqsTOY8NhuPx0ObyEienvuc25G8yliYTu7mTRQVFfHQgw8w5Zaf+eXl+7GjR7JzZx7v79vLjTfdzFWp4zhw4FNiYi52NIex1tbPwMa8aa3tXdPj4yak2Xnz0+tl3f/p6uvftL4YY9yOcE7aXfei2xHOyeEXJrkdQeS8lJUHzrFs0k+uZnFGuteDWb29sOGtbEVERP7TBM47CURERAKYCldERMQBKlwREREHqHBFREQcoMIVERFxgApXRETEAdX+xxfGmIXAd38BygDWWptW76lEREQamJr+p6npjqYQERFp4KotXGvtJwCm4r8AGgJ0oOIMF+AFZ6KJiIg0HLX9X8oLqPhc25HAv4AoVLgiIiLnrLY3TbW11k4DDlX+HepAJhERkQantsItNcZ4gOPGmFuBwPg0ZBERET9TW+H+pHKZW6k4u9XHjoiIiJyH2l7D7XHW7TeBsHrMIiIi0mDVVrhXVP5tgP8HlAAb6jWRiIhIA+S1cK21vz37a2PM0vqNIyIi0jB5LVxjTMxZX0YDXeo1jfhExa9PB47y8u/+p2b+7fALgfVWhh7TV7od4ZzsmT3S7Qh1Fmj7mrWBta95Amjz1iVqbZeU76j82wLH0JumREREzktthfuatTb9zBfGmJHAO/UbSUREpOGp7deCbv3O11PqK4iIiEhDVtOnBU0Bbga6G2O2UnF5uhxY71w0ERGRhqOmDy94GnjaGDPGWqt3JouIiPxAtV1SHnvmhqnwbD3nERERaZBqK9zoMzdsxfvJL6jfOCIiIg1TbYVbZIy5yhjTyhgzGihyIpSIiEhDU9uvBd0IzKTiDVRfA7n1nkhERKQBqu0MtyVwhIoPLcgHPqz3RCIiIg1QTb8WNANIoqJgXwESrLU3ORlMRESkIanpDHcUcBRYBrxBxe/gioiIyHmqtnCttbHAnUB3YAlwceWbp8KdDCciItJQ1PgarrX2oLX2/6y1o4CBVHxS0DKngomIiDQktb1pCgBr7WfW2j9bawfXdyAREZGGqE6FKyIiIj+MCldERMQBDapwF7wyn+j2kW7HqJOioiLiB/ajTXhzdu/a5XacWs24czpJCfFMnjSRkpISt+N8T1FREYPj+tO2VQt2767YnkOTBjM8JZEhg2Or7vNH/joXjIHHfnIZ6b8cwIJfDqBz61BeuW0A6b8cwCu3DSC1d0cAhv1XFGt/m8DyX8e5nPj7ysvLmXLjZJITB5EyZDAffRgY/5VAoBzLPtm/n84d2jIsOZFhyYkcPXrU7Ug18oesPi1cY0wXY8xRY8z6yj+OzZjy8nKWLM6gU6fo2hf2AyEhISxetpLUsePdjlKrvO3bOXzoEGvXv07PmBgWL8pwO9L3hISEkLFkBWPO2p7/WpXFqsxs7nvgDzzxlzkupvPOX+dCTMeWNA7ykPb4Zh5f/T7Xx3cBYPI/tvI/T2xmyZufAbDlgy8Z/kiOi0lrtmNHHsXFxWRlb2DGzLt58u9PuB2pVoF2LIuLH8zqrGxWZ2UTGenfTxLczlofZ7g51tqEyj+OPYVYMP9lUseOx+MJjJP2oKAgv5+cZ2zJ3UxyylAAUoYOJ3fzJpcTfV912zM4OBiAosJCLr74Ujdi1Ym/zoVD+aeqbrcMCebLEyWUl1uevaUvT9/Um44RIQDknzzN6TLrVkyvOnbsBIC1loL8fNq08b/t/F2BdizbvHkjyYmDmHX3TCo+48Z/uZ21Pv5FY40xrxtj/mCMMfUw/veUlZWxKCOdCWlXO7G6/zgFBfm0aNkSgLCwMI4fP+Zyoro5evQoSQlx3P6rXxAbP8jtOAHn2FcllFtY+9sEfju6JxlbD/Dz597i6sc380z2R9w79mK3I9aqTZs2eDweel0aw10zf8O11012O5JXgXYsi2rfnl173idzXQ5Hjx5h2dIlbkeqkT9k9XXhfg5cBAwC2gKpZz9ojJlgjEk3xqQfPHDAZyudP+8lxo1PC5hnhIEmPDyCosJCAPLz84mIaOVyorqJjIxk7fo3ePmVDO793V1uxwk4g3tEcup0GUkPrefWZ9/kd2NiyD95GoAtHx6jXVhTlxPWLnPNapqGNCVv1x5eXpDBjDumuR3Jq0A7ljVp0oRmzZphjGFM6jh27shzO1KN/CGrT/9VrbXF1tqvKj87dxFw+XceX2itTbPWpnWK9t3rE3v2vMu8l15g9MjhfPDB+9wxbarPxhbo268/WZlrAMjKXM2AgbEuJ6pdaWkp5eUV/yNpy5ZhNGvWzOVEgamgsmALvy6lZUgwzZtU/PfrXds1p+Dr025Gq7OIiAgAwsPCyS/IdzmNd4F2LCsq+uYTWze+voGuXS9yMY13/pC1to/nOyfGmBbW2jM/1SBgjy/Hr8mDDz1cdTu2X29mP+a/b5A525hRI9ixI499+/Zy05RbmHT9ZLcjVevyXr1oFxVFUkI80dGdmTrtDrcjVSt19Eh27szj/X17uWLESNZmZeLxeDDGw5w/+/ebZfxxLmx47yipvTux4LYBNA7y8MDSd3n5F/05dboMgFkZFe+o7n9Ra25LuYgL2jbjpZ/149fz8jhSWOxm9CrJKUOZP+8lhiYlUFxczMOzH3M7kleBdizbtPEN7rvnd4SGhtKlywXMuu8BtyPVyB+yGl++cGyMuQL4PXAS+Bi40VpbWt2y4yak2Xnz0322bglc5eX+/UaL7/J4HHlrgs/0mL7S7QjnZM/skW5HqDOH3qbiM/7+pqZAdu01V7MoI93rhPDpGa619jXgNV+OKSIi0hAExivzIiIiAU6FKyIi4gAVroiIiANUuCIiIg5Q4YqIiDhAhSsiIuIAFa6IiIgDVLgiIiIOUOGKiIg4QIUrIiLiABWuiIiIA1S4IiIiDlDhioiIOECFKyIi4gAVroiIiANUuCIiIg5Q4YqIiDggyLU1W7DWurb6c2WMcTtCg+XxaNvWpz2zR7od4Zx0uPFltyPU2efPTnQ7QoMWUMfdOkTVGa6IiIgDVLgiIiIOUOGKiIg4QIUrIiLiABWuiIiIA1S4IiIiDlDhioiIOECFKyIi4gAVroiIiANUuCIiIg5Q4YqIiDhAhSsiIuIAFa6IiIgDVLgiIiIOUOGKiIg4QIUrIiLiABWuiIiIAxpM4W7btpVhyYkMS07ksot7cOf0qW5HqtHuXbtIHBRLypDBpI4eyYkTJ9yO5FVRURHxA/vRJrw5u3ftcjtOrQItb6DNhw056xkxLJmhSQmsWL7M7ThVPMbw1M8GsmJmMn+9uT9BjQz/uiuFFTOTWT1rKD07hQFwU3I3dvzpKp77ZbzLib8v0ObuJ/v307lD26pj79GjR92O5NWMO6eTlBDP5EkTKSkpcXz9Pi9cY0yCMWatMSbHGHOVr8evSZ8+fVmdlc3qrGwGxsUxavQYp1Z9zrp17072ho1krsuhd5++LF+6xO1IXoWEhLB42UpSx453O0qdBFreQJoPp06d4s9z/sTSFa+yZu16Ro12bBev1ag+0ew/coJRf8hi72eFjOrdmav+mMWoP2RxX3oePxveA4ClWz/hqj+udTlt9QJt7gLExQ+uOvZGRka6HadGedu3c/jQIdauf52eMTEsXpTheAafFq4xpikwDbjCWjvYWuv409/S0lK2bdlCbJz/PXs9Izg4uOr2yZMn6da9h4tpahcUFOTXO9J3BVreQJoPuZs3ERISwvjU0Vw9fiyHDh1yO1KVLpHNeeeT4wDs2H+MAd0jKS2zALQMCebdA/kAfFFYTFm5dS2nN4E2dwE2b95IcuIgZt09E2v9c7sCbMndTHLKUABShg4nd/MmxzP4+gx3IPA1sMIYs8QYE+Xj8Wu1PnsdcYMG4fH499XytVmZ9O/di5z12VzYtavbccRlgTIfjhw+zP79H5OxZDk33jSFBx+41+1IVfb9u4BBF1ccchIujiIstDGtWzRh1e+G8uj1fdj03hGXEzY8Ue3bs2vP+2Suy+Ho0SMs8+OrMwUF+bRo2RKAsLAwjh8/5ngGX7dSO+ACYBTwFHDv2Q8aYyYYY9KNMekHDx7w8aorLF60kLHjJtTL2L6UlJxC7pvbSR03nrnPPOV2HHFZoMyHsPBwBsbG0rhxYxISh/Denj1uR6qyKu8zSkrLWP7bJEKbBHG44Gu+LCpm+ANruO4vr/O7tMvdjtjgNGnShGbNmmGMYUzqOHbuyHM7Uo3CwyMoKiwEID8/n4iIVo5n8HXh5gNvWGtLgHVAzNkPWmsXWmvTrLVpnTpF+3jVFZeTt+bmEhc/yOdj+1JxcXHV7bCWYTQLbeZiGnFbIM2H3n36VpXsjrztXHDBhS4n+oa1cNe8txn90FqOnSjm1bcOYkzFY4UnSzhZXOpuwAaoqKio6vbG1zfQtetFLqbxrm+//mRlrgEgK3M1AwbGOp4hyMfjbQVur7zdC/jIx+N7lbM+m9j4+IC4nDznsdl4PB7aREby9Nzn3I5UqzGjRrBjRx779u3lpim3MOn6yW5H8iqQ8gbSfGjdujUjrhxFypDBeDwennxqrtuRqrQNa8rcX8RRWlZOzu5DfHL0BCtmJlNebim3cMfz2wAY2/9HTEnuxoVRLVjymyGMfWQd/vTSYyDN3U0b3+C+e35HaGgoXbpcwKz7HnA7Uo0u79WLdlFRJCXEEx3dmanT7nA8g/H1i9zGmF8AVwPlwI3W2mpLd9z4NPvS/AU+XXd9MmeeKosEGH9+I0t1Otz4stsR6uzzZye6HeGcBNpcCKTj7sRr0li0MN1rYF+f4WKt/SvwV1+PKyIiEsj8+9qriIhIA6HCFRERcYAKV0RExAEqXBEREQeocEVERBygwhUREXGACldERMQBKlwREREHqHBFREQcoMIVERFxgApXRETEASpcERERB6hwRUREHKDCFRERcYAKV0RExAEqXBEREQeocEVERBwQ5NqaDRhjXFv9uSovt25HaLACaBoAgTVvIfDyfv7sRLcj1FnE+KfcjnBOjmfc7HaEc2JtAB136xBVZ7giIiIOUOGKiIg4QIUrIiLiABWuiIiIA1S4IiIiDlDhioiIOECFKyIi4gAVroiIiANUuCIiIg5Q4YqIiDhAhSsiIuIAFa6IiIgDVLgiIiIOUOGKiIg4QIUrIiLiABWuiIiIA1S4IiIiDmgwhTvjzukkJcQzedJESkpK3I7jVVlZGTdcfy3DUxK5+aYbOH36tNuRqlVUVMTguP60bdWC3bt3AXDLlBv5Uce2PPm3J1xOV7Nt27YyLDmRYcmJXHZxD+6cPtXtSF69/dZbJCXEkzJkMBOvSfPb+QCBlfUMfzw2eDyGZ6cmsvr3V/LU/w4mqJHh1hEX8/ojY9jwyBhG9OkMQPdO4ax7aDSZD47irz+Pdzn19xUVFRE/sB9twpuze9cut+N4VV5ezpQbJ5OcOIiUIYP56MMPHc/g08I1xvQ1xqyv/LPXGDPHl+PXJG/7dg4fOsTa9a/TMyaGxYsynFjteVu2dDEXXHABqzKz6dGjJ8uWLnY7UrVCQkLIWLKCMWPHV9137/0P8vuHHnExVe369OnL6qxsVmdlMzAujlGjx7gdyasOHTuy4tXVZK7LoWvXi1i+bKnbkWoUSFnBf48NY/p34ePDRQy7eyXvHchnTP8LuPmKGBJmLGPkPf/iznG9ALh5eAwPpb9Nyl0rCG0SxKVdWrmc/NtCQkJYvGwlqWcdI/zVjh15FBcXk5W9gRkz7+bJvzt/0uDTwrXWbrXWJlhrE4DXAUf2xi25m0lOGQpAytDh5G7e5MRqz9vHH3/Ef112OQCX9/p/bHrjdZcTVS8oKIjIyMhv3de+fXuX0py70tJStm3ZQmyc/50ZnC0qKorQ0FAAgoODCQoKcjlRzQIpK/jvseGCdi3Z+fGXAGz/8AtiY9rz4eeFhDQOokVIMF8WnQJgz4HjhIU2BqBZ02COnyh2LXN1qjtG+KuOHTsBYK2lID+fNm2cz10vl5SNMUFAfypKt94VFOTTomVLAMLCwjh+/JgTqz1vPbr3JCd7HQDZ67LILyhwOVHDtD57HXGDBuHxBMYrJ59++inr1mYxYuSVbkepVaBk9ddjw3sH8xl8aQcAhlzWkbBmjcncfoDtT0zgjUdT+du/Ki7PrtvxGfdN6kveE2kUfFXCwS++cjN2QGvTpg0ej4del8Zw18zfcO11kx3PUF9HoiFAjrW2/Ow7jTETjDHpxpj0gwcO+Gxl4eERFBUWApCfn09EhH9ddvmuK0ZeSXDjxlwxdAgnv/qKdu3auR2pQVq8aCFjx01wO0adFBYW8tPJk3jqmWcJDg52O45XgZTVX48Nr775CadLy1n1wJU0axrE0YKvuXFoDy752QIu+0U6913bF4D7ru3DzX9Zz+W3pVN4soSUXp1cTh64MtespmlIU/J27eHlBRnMuGOa4xnqq3AnAAu/e6e1dqG1Ns1am9YpOtpnK+vbrz9ZmWsAyMpczYCBsT4buz54PB4eeXQOr61ZR6vWrf3+NcZAVFpaytbcXOLiB7kdpVZlZWXccN1EZt49ix936+Z2HK8CKSv477HBWrjzn5sZ/ruVfFlUzLLcjykuKaP4dBkni0tpEtwIYyqWPXMZOf+rYsKbNXExdeCLiIgAIDwsnPyCfMfX7/MXYCovJw8AbvH12DW5vFcv2kVFkZQQT3R0Z6ZOu8OpVZ+XQ4cOMXnSNTRqFERiUhIDY+PcjlSj1NEj2bkzj/f37eXGm27m/X17eXXlCsrKyvjoow955FFH3hd3znLWZxMbHx8Ql5MzFqaTu3kTRUVFPPTgA0y55WdMSLva7VjVCqSs4L/HhnbhIbwwPYnSsnKyd3zGpj2HWbLpY3IevopGHsM/Xt2NtfBIxnYevzWesvJy8r8q4ZGMPLejf8+YUSPYsSOPffv2ctOUW5h0/WS3I1UrOWUo8+e9xNCkBIqLi3l49mOOZzDWWt8OaEwKkGqt/bm35cZNSLPz5qf7dN31qbzct9tJvnHmmXygMIEWWOpNxPin3I5wTo5n3Ox2hHPi636qT9deczWLMtK9Hhx8foZrrc0EMn09roiISCDz/+ttIiIiDYAKV0RExAEqXBEREQeocEVERBygwhUREXGACldERMQBKlwREREHqHBFREQcoMIVERFxgApXRETEASpcERERB6hwRUREHKDCFRERcYAKV0RExAEqXBEREQeocEVERBzg8w+gb6g8HuN2hDqz1rod4ZwEWFwgsAIbEzhzFwJr/h5bOMXtCOek/Q3z3I5wTj5/dqLbEequDruZznBFREQcoMIVERFxgApXRETEASpcERERB6hwRUREHKDCFRERcYAKV0RExAEqXBEREQeocEVERBygwhUREXGACldERMQBKlwREREHqHBFREQcoMIVERFxgApXRETEASpcERERB6hwRUREHKDCFRERcUCDKNzdu3aROCiWlCGDSR09khMnTrgdqVZFRUXED+xHm/Dm7N61y+04Xn2yfz+dO7RlWHIiw5ITOXr0qNuRvqeoqIjBcf1p26oFu3dXbM+MhQtIHDSQK4YlcfDAAZcT1iwQtu8Zgbavbdu2tWq7XnZxD+6cPtXtSF7561zwGMNTPxvIipnJ/PXm/gQ1MvzrrhRWzExm9ayh9OwUBsAj1/XmtbtTWHPPMIZc2t7l1N/mD3PXp4VrjPEYY543xrxujNlgjOnqy/Fr0q17d7I3bCRzXQ69+/Rl+dIlTqz2BwkJCWHxspWkjh3vdpQ6iYsfzOqsbFZnZRMZGel2nO8JCQkhY8kKxlRuz9OnT/P4n+ewOms9s+65nz/+4QGXE3rn79v3jEDb1/r06Vu1XQfGxTFq9Bi3I9XKH+fCqD7R7D9yglF/yGLvZ4WM6t2Zq/6Yxag/ZHFfeh4/G94DgL++9h5X/D6TCbOzmTnuv1xO/W3+MHd9fYZ7OdDEWhsPPADc5uPxqxUcHFx1++TJk3Tr3sOJ1f4gQUFBfrMz1cXmzRtJThzErLtnYq11O873fHd7fvDB+/SMiaFx48YMGBhbddbrr/x9+54RiPsaQGlpKdu2bCE2Lt7tKLXyx7nQJbI573xyHIAd+48xoHskpWUV2VqGBPPugXwAPjlacdZYUlpGuX9Er+IPc9fXhXsQwBhjgHDAsesha7My6d+7Fznrs7mwqyMn1v8xotq3Z9ee98lcl8PRo0dY5udnNQAF+fm0aNGy6uuysjIX03gXaNs3EPe19dnriBs0CI/Hv19F89e5sO/fBQy6OAqAhIujCAttTOsWTVj1u6E8en0fNr135FvL3z3hMp5as9eNqF65PXd9Pfu+AMqBPcAjwHNnP2iMmWCMSTfGpPv6NbWk5BRy39xO6rjxzH3mKZ+O/Z+uSZMmNGvWDGMMY1LHsXNHntuRahUeHkFRUWHV140aNXIxjXeBtn0DcV9bvGghY8dNcDtGrfx1LqzK+4yS0jKW/zaJ0CZBHC74mi+Lihn+wBqu+8vr/C7t8qplfxJ/IY2DGpGxeb9reWvi9tz1deEOA7621vYAxgF/OvtBa+1Ca22atTatU3S0z1ZaXFxcdTusZRjNQpv5bGypeEPSGRtf30DXrhe5mKZuul50Ee/t2UNJSQmbN23kkksudTtSjQJp+wbivlZaWsrW3Fzi4ge5HaVW/joXrIW75r3N6IfWcuxEMa++dRBjKh4rPFnCyeJSAOJ6tmN0n87MePFNF9NWzx/mblA9jHm88u98Ki4r17u1WZnMeWw2Ho+HNpGRPD33OSdW+4ONGTWCHTvy2LdvLzdNuYVJ1092O1K1Nm18g/vu+R2hoaF06XIBs+7zzzcgpY4eyc6deby/by833nQzv/jlrxiWnEDTpk15eu7zbserUaBsXwjMfS1nfTax8fF+fzkZ/HcutA1rytxfxFFaVk7O7kN8cvQEK2YmU15uKbdwx/PbAPjTDX356tRpls5I4lRJGRMezXY5+Tf8Ye4aX74ob4xpBLwIdASaAL+21m6qbtlxE9LsvPnpPlu3fMNf3mhRVwEWt+qZfaAwARY40OZvIOlw48tuRzgnnz870e0IdTbxmjQWLUz3urP59AzXWlsG/MSXY4qIiDQE/n+NRUREpAFQ4YqIiDhAhSsiIuIAFa6IiIgDVLgiIiIOUOGKiIg4QIUrIiLiABWuiIiIA1S4IiIiDlDhioiIOECFKyIi4gAVroiIiANUuCIiIg5Q4YqIiDhAhSsiIuIAFa6IiIgDfPoB9OfEgrXWtdU3ZMYYtyOck/IAmwfl5YGVN6hRYM2HQJq/gXYM+/c/f+J2hHPS7roX3Y5QZye3fFLrMjrDFRERcYAKV0RExAEqXBEREQeocEVERBygwhUREXGACldERMQBKlwREREHqHBFREQcoMIVERFxgApXRETEASpcERERB6hwRUREHKDCFRERcYAKV0RExAEqXBEREQeocEVERBygwhUREXFAgyjcT/bvp3OHtgxLTmRYciJHjx51O5JXgZa3qKiI+IH9aBPenN27drkdp1pFRUUkxPWnXasW7N5dkfGymG4MT0lkeEoi67IyXU74bdvffouhQwYzPDmR6yZezenTp1m0cAFDBscyclgyBw8ccDtitXbv2kXioFhShgwmdfRITpw44XYkrwJh7p6tvLycKTdOJjlxEClDBvPRhx+6Hckrfz2WeYzh6V/EsfLuFP52y0CCGhkAOrUO5cjzP6Fnp3AAGgd5+L+f9mPFXSnM+3VCvecK8uVgxphGwAtAR2A/MMVae9qX66hJXPxgXl6w0IlV+UQg5Q0JCWHxspXM/M0dbkepUUhICAuXrODu395ZdV/LsDBWZWa7mKpmHTp0ZOnK1wgNDeXe393FyuVLeeIv/8fqtTm89eY2Hn7o9zz+t3+4HfN7unXvTvaGjQA8+MB9LF+6hJ9cO8nlVDULhLl7th078iguLiYrewNrszJ58u9P8Mijc9yO5ZU/HstG941m/5Eipvz1DX51ZQyj+3Rmce4n3D7qErbs++ZJwS3DerB6+2e89vYWR3L5+gx3LPCRtTYBeLfya0ds3ryR5MRBzLp7JtZap1Z73gIpb1BQEJGRkW7H8Kq6jCdOnGBYcgI3XDeRY8eOuZSseu2ioggNDQUgODiYD95/nx49Y2jcuDEDBsZWnaX7m+Dg4KrbJ0+epFv3Hi6mqV0gzN2zdezYCQBrLQX5+bRp4//Z/fFY1qVtC97ZfxyAHfuPMaBHW34U2RyL5cAXX1Utl3xZBwb2aMvKu1O4YciP6z2Xrwv3QiCv8vbbQLyPx69WVPv27NrzPpnrcjh69AjLli5xYrXnLdDyBqqs9W+wOms9KUOH8YcH7nU7TrUOfPop2evWMmBgLC1btqy6v7yszMVU3q3NyqR/717krM/mwq5d3Y7ToLRp0waPx0OvS2O4a+ZvuPa6yW5H8spfj2V7Pytg0MVRACRc0p6w0MbcPupiHl/57reW69gqlK3vH+WqP2QxPvYCOrQKrddcvi7cPcCQytvJQLiPx69WkyZNaNasGcYYxqSOY+eOvNq/yUWBljdQtW7dGoDUcRPYuXOHy2m+r7CwkCk3Xs/fn55Lm7ZtKSwsrHrM06iRi8m8S0pOIffN7aSOG8/cZ55yO06DkrlmNU1DmpK3aw8vL8hgxh3T3I7klcS96rIAAAjcSURBVL8ey1ZtP0hJaTkr7kohtEkQoU0qXj399KyzW4CCk6fJ2XWIsnLL1n1HuSiq5f9v796DtKrrOI6/P4CCuyYwiWLDzYBKLjOE5oU0ELM74qCiFSqTGjbdpixknByVtEgGaTK1kcEcQ5GL2iQicRFQMLyGWIhONg0DiGiI0Wi7uHz745yFRZ5ddp3znPM86+f1z549Z8/v+TzPnv19z+13tlRzmcm64C4C6iWtBGqB7U0XSrpA0nxJ87dsye6mkN27d++bXvvE4/TvPyCztsuh2vJWo/r6eurq6oDGz7iyjsQaGhq4bOIEplzzMwYO/AT9+w9g06aXqK+v5y9PrmXIkKFFRyyp8TMF6HpUV2pragtM0z51794dgG5du7Hr7V0Fp2lZpfZlEXDNnGcZc9Mydv63jg3/2smnenXlgatHc+bQ45h52Sl06ijWvbKDoX2Tz3tI3+5sfrO8NwGqXOfcJV0PLI+INaWWn3f++Jgzd14mr/XnJY9yw3XXUlNTQ79+x/O7WbPp1CnT+8EyVe68kjJrq9G5Y77CCy+sp0+fvlx+xSQuvnRiZm037M1mGxx3zlfZsCHJ+LVzxvLgwgXU1NbS+fDO3HHnbHr17p3J62TxN7Ng3lyu+tEPGDw4KayXf3sSEcHtt91Kl85duHP23Znl7dQxu/3qxY8sYuaM6XTo0IGje/Rg1uy7912LrlTl3Haz7j+THbFL2LZtK3V1dfxq+gxOPW1Epq+RpXL3ZT0vnfOB1jumaxfu+v4ZNOwNVr34GjMf/vu+ZbdPGsGtj2zkpS276HFUF+64cgQfOeIwlm/YxvSHXvzAWd9Z81v2bH6qxc4304IrqSdwP/AeSbGd1tzPZllw7UDlKLjllFXBzUul3BjSWlkWXDtQtW0L1eaDFtwitKbgZnoYGBHbgVFZtmlmZtYeeNfXzMwsBy64ZmZmOXDBNTMzy4ELrpmZWQ5ccM3MzHLggmtmZpYDF1wzM7McuOCamZnlwAXXzMwsBy64ZmZmOXDBNTMzy4ELrpmZWQ5ccM3MzHLggmtmZpYDF1wzM7McuOCamZnlwAXXzMwsB4qIYl5Yeg54NeNmewFbMm6znJy3vJy3fKopKzhvuVVT3nJl7R8RJ7b0A4UV3HKQND8ixhedo7Wct7yct3yqKSs4b7lVU94is/qUspmZWQ7aW8FdUHSANnLe8nLe8qmmrOC85VZNeQvL2q5OKZuZmVWq9naEa2ZmVpFccM3MzHLggmtmZpaDqi24kjpJukjSbyTNSb9eKKlT0dnaStIlRWd4P0kdJZ0naaykjk3mX1BkruZIqpH0TUknSuomabKk70rqUnS21pA0o+gMzZH00SbT50q6XtIESRXZf0gak349RtIdklZJuktSv2KTlSZphqQzis7RWpKOl3SLpCmSekqaK2mxpFOLzvZ+aT92QVof7pF0s6QRheWp1pumJP0BeAlYArwNdAO+CJwQERcXma05kgaVmg3cGRGfzTtPSyTdT/Jgkj3AWcDEiHhV0mMRMbrYdAeT9DDwDFADjAIeINkuRkfERQVGO4ikHcBmYC/J7x9gIPBKRJxcWLBmNP7OJd0E9AAeBD4HfCwiJhYargRJKyPiTEkPALOAx4DhwC8qdNvdBDwBnAqsAhZExOOFhmqBpFXAjUB3YBpwHvAfYE5EFFbMSpF0F7AR+CvweZL+YSfwbkTcnHeeqjsabKJvicL6nKSK3VCBdcBC9neyjfoWkOVQjm0sVOlGe4+kGwrO1JIjI2IqgKTnI2J6On1hsbFK+iFJJ7UgIuYBSHo0Ir5cbKxDOj0iRqbTSyStLjRN8xrSM121wPKIeA9Y1/RMTYV5LSKuSPONBiZIug1YHRHfKzhbKR0iYjmApKkRsT6dri82Vkn9IuJb6fQKSSsi4ixJSwEX3DZ4StI9wFKSvaujgC8ATxWaqmUbgZ9GxL+bzpT0SEF5WtJRUpeI+F9EbE5P090LDC06WDMOazI9ucl0xXWyETE3PYPwDUl/BOZz8E5YJRkiaT4wUNIREfFuOr+2yFAt+DnJUfjbwFpJa4BBwP2FpjqEiGgAlgHL0uJ7ZsGRmvNPSfel089LmgW8BWwtMFNztqUHCutJdmbWp/MPa36V8qnaU8oAkoYBp5GcTn4LWNe4t1WJJB0JvBMRe4vOciiShgNbImJHk3kdgPERUXEdl6TBwKa002qcdzhwdkRU4g4NsO8znQAMiogpRecpRVLTMzDbImJPui2fHhFLisrVEkmdgRHAscAu4OmI2FlsqtIkfSYinik6R2tJEjCM5HnEb5JcygNYWml9W/r3NQ74OMklyEUREZKOjYjXc89TzQXXzMysWlTkXYZmZmbtjQuuWYWR1E/SG+lwlrWSBrRx/SslTUyHbJS80S0dOtXq/5giaZqkUW3JYWYHcsE1q0yrI2IUcAtwdePMtox9jYjtEXFdM4u7AVXx79TM2gsXXLPK9jeSYSKLJP0JuFjSSZJWSnpC0k8AJPWRtEbSYpIxso1HygvT6ZPTn18t6SrgO8DI9Cj6k5K+lC5/UtLX03WGSXomHeN8QhFv3qw9qeZhQWYfBmcAL5MMexuZ3mG5AhgXEW9Jeih9CMxkYGpELJV0b4l2ZpLcYb41PUruA/SPiPPT739P8sCQ94CV6TCgG0nuoH4FqOTx7WZVwQXXrDKNTJ/osxP4JXBK7B9SMBR4KBmdQXegNzAAeC5d/nSJ9g6PiK0AEbE3XbfR0SRPulra5PseJA8/eRlA0rPZvC2zDy+fUjarTKsjYlREjANeJ3kMZKMXgLHpNd7hJIX2H8Cn0+UnlWivTtJxsO868B72PxTkTZIximenbQ6LiO3A65IGpuMuh2f55sw+jHyEa1Z9pgAPpoWzHjiX5DF196XXdHeVWOfHwML08XsPA78Gjkiv8U4GbgKWS9oLvEFyQ9W1wH3AjmbaNLM28IMvzMzMcuBTymZmZjlwwTUzM8uBC66ZmVkOXHDNzMxy4IJrZmaWAxdcMzOzHLjgmpmZ5eD/wyHbY52SSOcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interp = ClassificationInterpretation.from_learner(learn)\n",
    "losses,idxs = interp.top_losses()\n",
    "interp.plot_confusion_matrix(figsize=(8,8), dpi=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jetzt können wir uns noch die Beispiele anschauen, die das Netz nicht richtig klassifiziert hat. Gibt es dabei Beispiele die auch als Mensch schwer zu klassfizieren sind? \n",
    "\n",
    "Anmerkung: `heatmap=False` weil wir für unser Netz keine \"heat map\" mit dem Standardverfahren berechnen können, da es eine bestimmte Netzstruktur voraussetzt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x792 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interp.plot_top_losses(36, figsize=(15,11), heatmap=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aufgaben und Ideen: eigene Bilder\n",
    "\n",
    "1. Erstelle ein Netz, das Deine Bilder verarbeiten kann. Erstelle dafür eine Kopie des ganzen Notebooks oder kopiere die benötigten Zellen in einen neuen Abschnitt. Achte darauf, dass Du die richtigen Dimensionen wählst. Wenn Du farbige Bilder verarbeiten möchtest, brauchst Du für die Input-Layer einen Vektor der Größe `width * height * 3`. Welche Größe ist für Deine Bilder passend? Eine klassische Größe wäre 224x224 Pixel. Hast Du getrennte Trainings- und Testdaten? Wenn nein, gibt es z.B. auch die Funktion `split_by_rand_pct` um deine Daten zufällig zu splitten.\n",
    "2. Trainiere das Netz.\n",
    "3. Wie sind die Ergebnisse? Wie sieht die Konfusionsmatrix aus? Wie hoch wäre die Fehlerrate, wenn Du für die Vorhersage einer Klasse zufällig wählst? Ist die Vorhersage des Netzes wesentlich besser als eine zufällige Vorhersage?\n",
    "4. Was passiert wenn Du mehr Neuronen in die Schichten konfigurierst?\n",
    "5. Was passiert wenn Du andere Aktivierungsfunktionen nutzt? (Beispiele: `torch.sigmoid`, `torch.relu`, `torch.tanh`)\n",
    "6. Was passiert wenn Du noch ein paar Epochen länger trainierst?\n",
    "\n",
    "## Aufgaben und Ideen: MNIST\n",
    "\n",
    "Bei Deinen Bildern war es wahrscheinlich nicht so einfach gute Ergebnisse zu erzielen. Kannst Du die Ergebnisse auf dem MNIST-Datensatz verbessern? Schaffst Du eine Fehlerrate von 2% oder vielleicht sogar 1%?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}